A Bayesian Method for Adverse Effects Estimation in Observational Studies with Truncation by Death
死亡截断下观察性研究不良事件效应的贝叶斯估计AI 解读 ↓
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死亡截断下观察性研究不良事件效应的贝叶斯估计
- 研究问题
- 观察性研究中死亡使不良事件结果受到截断，如何同时比较干预对死亡和不良事件的影响，而不只关注双重存活人群？
- 方法与思路
- 作者用贝叶斯方法填补每位受试者在未接受干预下的死亡及不良事件结果，再构造严重程度递增的复合有序结局进行比较。
- 材料陈述的贡献
- 摘要提出覆盖整个样本的死亡与不良事件联合评价方式，并将其用于比较两类降糖药治疗老年患者时的心力衰竭发生情况。
- 阅读关注点
- 应核对反事实填补依赖的混杂控制与模型假设、先验设定、死亡截断的处理，以及复合结局排序和目标效应的临床解释。
- 阅读边界
- 这是 arXiv 预印本，同行评审与期刊接收均未核实；摘要未说明反事实联合分布的识别依据，也未报告药物比较结果。
原文摘要
Death among subjects is common in observational studies evaluating the causal effects of interventions among geriatric or severely ill patients. High mortality rates complicate the comparison of the prevalence of adverse events (AEs) between interventions. This problem is often referred to as outcome "truncation" by death. A possible solution is to estimate the survivor average causal effect (SACE), an estimand that evaluates the effects of interventions among those who would have survived under both treatment assignments. However, because the SACE does not include subjects who would have died under one or both arms, it does not consider the relationship between AEs and death. We propose a Bayesian method which imputes the unobserved mortality and AE outcomes for each participant under the intervention they did not receive. Using the imputed outcomes we define a composite ordinal outcome for each patient, combining the occurrence of death and the AE in an increasing scale of severity. This allows for the comparison of the effects of the interventions on death and the AE simultaneously among the entire sample. We implement the procedure to analyze the incidence of heart failure among geriatric patients being treated for Type II diabetes with sulfonylureas or dipeptidyl peptidase-4 inhibitors.